13 posts tagged with "comparison"

TimescaleDB is an open-source time-series database optimized for fast ingest and complex queries. It is engineered up from PostgreSQL and offers the power, reliability, and ease-of-use of a relational database, combined with the scalability typically seen in NoSQL systems. It is particularly suited for storing and analyzing things that happen over time, such as metrics, events, and real-time analytics.

Since TimescaleDB is based on PostgreSQL, it supports blob data and can be used to store a history of unstructured data such as images, binary sensor data, or large text documents. In this article, we will use the database as a time-series blob storage and compare its performance with ReductStore, which is designed specifically for this use case.

TimescaleDB and ReductStore both have Python Client SDKs. We'll create simple Python functions to read and write data, then compare performance with different blob sizes. To repeat these benchmarks on your own machine, use this repository.

InfluxDB works well for robotics metrics such as battery level, temperature, CPU usage, detection counts, and anomaly scores. These numeric values are a good fit for dashboards, alerts, and time-series analytics.

Robots also generate much heavier data: camera frames, LiDAR scans, audio, and high-frequency waveform chunks. This raw data must often be preserved in its original format for incident replay, debugging, and model training.

You do not need to replace InfluxDB. Keep it for the metrics you need to visualize and analyze, and add ReductStore for the raw sensor data. Use the same event time and stable robot and sensor identifiers in both systems so a metric can be traced back to the original records.

Modern robots collect a lot of data from sensors, cameras, logs, and system outputs. Managing this data well is important for debugging, performance tracking, and training machine learning models.

Over the past few years, we've been building a storage system from scratch. As part of that work, we spoke with many robotics teams across different industries to understand their challenges with data management.

Here's what we heard often:

• Only a subset of what robots generate is actually useful
• Network connections are not always stable or fast
• On-device storage is limited (hard drive swaps is not practical)
• Teams rely on manual workflows with scripts and raw files
• It's hard to find and extract the right data later
• ROS bag files get large quickly and are difficult to manage

In this article, we compare four tools built to handle robotics data: ReductStore, Foxglove, Rerun, and Heex. We look at how they work, what they're good at, and which use cases they support.

If you're working with robots and need to organize, stream, or store data more effectively, this overview should help.